As the most advanced scientific technology develops, various techniques are applied to military fields. Especially, developments of sensors and computer hardware enable an unmanned combat system.
Concerning technology developments in the field of an unmanned combat system, an autonomous vehicle performs supervising Reconnaissance, Surveillance and Target Acquisition (RSTA), commands and controls, explosive detections and removals, and so on. As a system of an individual autonomous vehicle is interworked with a wideband communication network, a plurality of autonomous vehicles systematically perform several functions at visible or invisible circumstances.
For driving toward a waypoint, the autonomous vehicle receives waypoints transmitted from a command and control vehicle or a portable control device thus to select the next traceable waypoint, and generates a steering command for tracking the selected next waypoint.
Generally, a steering command is generated by the two methods. One is model approach, and another is learning approach. The model approach is a method for controlling a speed and steering by using a dynamic model of a platform, and the learning approach is a method for controlling a speed and steering by performing specific learning offline. The specific learning is carried out by utilizing the output speed and information of a steering value with respect to an input speed, a steering command, information of attitude state, and information of the road surface state obtained through many experiments using a learning method such as a neural network.
A function of steering control based on the model approach is much influenced by the accuracy of a model. For real-time control, the model is simplified by linearization in this model approach. This may deteriorate reliability and stability when the model approach is applied to a real autonomous vehicle. Furthermore, the model approach may deteriorate a waypoint tracking performance when the surrounding circumstances change.
On the other hand, steering control based on the learning approach is more practical with respect to learned circumstances, but does not guarantee its performance with respect to non-learned circumstances.